Testing Mediated Moderation Models: Steps 3-5

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From the course by University of Virginia

Understanding Your Data: Analytical Tools

22 оценки

University of Virginia

Understanding Your Data: Analytical Tools

22 оценки

Are you trying to understand data from your research? Learn how and when to conduct mediation, moderation, and conditional indirect effects analyses? Or, perhaps, how to theorize and test your theoretical models? If so, this is the course for you! We will walk you through the steps of conducting multilevel analyses using a real dataset and provide articles and templates designed to facilitate your learning. You'll leave with the tools you need to analyze and interpret the results of the datasets you collect as a researcher. By the end of this course, you will understand the differences between mediation and moderation and between moderated mediation and mediated moderation models (conditional indirect effects), and the importance of multilevel analysis. Most important, you will be able to run mediation, moderation, conditional indirect effect and multilevel models and interpret the results. This course is supported by the BRAD Lab at the Darden School of Business, which studies organizational behavior, marketing, business ethics, judgment and decision-making, behavioral operations, and entrepreneurship, among other areas. More: http://www.darden.virginia.edu/brad-lab/

From the lesson

Conditional Indirect Effects

Now that you know more about mediation and moderation, let's take a look at conditional indirect effects models, which are a combination of mediation and moderation models. First we will get to the heart of the differences between moderated mediation and mediated moderation models.This will allow you to fully understand the relationships between independent variables and dependent variables. By the end of the module, you will be able to theorize about conditional indirect effect models on SPSS and to test which path of the mediation model is affected by the moderator. Then you'll dive into SPSS, run different models, and learn how to interpret the results.

Conditional Indirect Effects: Mediated Moderation Models8:17

Testing Mediated Moderation Models: Steps 1-25:48

Testing Mediated Moderation Models: Steps 3-56:50

Moderated Mediation Models Overview7:30

Testing First Stage Moderated Mediation Models8:55

Second Stage Moderated Mediation Models Overview5:11

Testing Second Stage Moderated Mediation Models9:57

Dual Stage Moderated Mediation Models Overview5:18

Testing Dual Stage Moderated Mediation Models11:27

Meet the Instructors

Cristiano Guarana
Post-Doctoral Researcher
Darden School of Business

I do not show the output for this particular model

because it should be similar to the one that I showed you just

before when we had the dependent variable as being job performance.

That's the same way that you should look at the output.

Look for an interaction term.

If that's significant, you'll start having initial support for

your overall condition of interact model.

Now, let's run model number 8, on PROCESS that macro developed by Hayes.

So now it is different.

You needed to select number 8.

Your model is model number 8 and you have job performance

as your independent variable meaningfulness as your

independent variable, engagement as your mediator.

And you'll need it to select and enter an organization

identification as your proposed moderator w.

It's important to enter this variable in the w moderator not in the z,

or v, or q because model number eight

requires your moderator to be moderator w.

Here again, yes, we keep it constant, because we are adding an interaction term.

So we needed to mean center the variables and

now you just double check that you have your moderators

on both mediator and dependent variable.

We do not have controls or conference in this model, but

if you have some in your own data set, it's important to partial out

those variants from the mediator and from the dependent variable as well.

Now click on OK and you'll have the output file.

The first thing that we do every single time that we run

a model on process is double check the model number.

So here is model number eight, which is good.

We are probably getting the results that we want.

At least we are conducting the analysis using the model that we need,

so now let's walk through all these coefficients.

First, the first thing that we look at, is the relationship

between the interaction term and our job engagement.

We do find here, that there is a significant relationship

between this interaction term and engagement.

This is last and 0.05.

The second thing that we look.

So this is the first, and that this is the second.

The second thing that we look at is the relationship between

our mediator and our dependent variable.

So in this case, job engagement has a significant relationship

with our dependent variable, job performance.

Again, p is less than 0.05.

In model number eight, we are controlling forwarding direction

term on the dependent variable and this is our interaction to here and

we see that the effects of the interaction term meaningfulness and

organization identification on performance is not significant.

P is more, is not less than 0.05.

You can see here, that the interaction term is meaningfulness and

organization identification.

Similar to here, job meaningfulness times organization identification,

that's interaction term, or interaction number one.

We are just labeling them differently because they are part

of a different analysis in our model and

now the next step is to look at the bootstrapping analysis.

And we look at the mediator here and we do find that

the relationship of job meaningfulness on

job performance via job engagement is significant for

individuals high in organizational identification, okay.

So and the reason that we know that this relationship is significant is because

there is no zero in the confidence interval of our bootstrap analysis but

we also see that for the relationship between job meaningfulness and

job performance via job engagement.

For individuals low on organization identification,

that relationship is not significant, because there is

zero in the confidence interval of our bootstrap analysis, okay?

So we do find support for our conditional indirect effect model here and

finally we look at the index of moderated mediation

that was recently actually added to the process macro by Hayes.

And again, there is a whole paper about what this coefficient, what this index is.

You can download this paper by going to the description of this video and

we do find it, that the bootstrapping analysis for

this index does not have zero.

The confidence interval for that particular test does not have zero.

More indication that we do have a conditional indirective fact,

a significant conditional indirective fact.

So in this session we conducted an analysis and

walk it through all the steps of getting this model running.

So we could find, in this particular case, that we do have

a significant condition or indirect effect of job meaningfulness and

job performance via our mediator, job engagement.

And that indirect fact changes as a a function of

the organization identification level of our employees.

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